Improved logistic regression model for diabetes prediction by integrating PCA and K-means techniques

Zhu, Changsheng; Idemudia, Christian Uwa; Wenfang, Feng

doi:10.1016/j.imu.2019.100179

Cited by 173 publications

(90 citation statements)

References 3 publications

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“…In [25], the authors integrate PCA and K-means algorithm to predict diabetes. In this work, the authors first applied PCA for dimensional reduction and then applied K-means to cluster the data.…”

Section: Literature Reviewmentioning

confidence: 99%

Analysis of Dimensionality Reduction Techniques on Big Data

et al. 2020

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Due to digitization, a huge volume of data is being generated across several sectors such as healthcare, production, sales, IoT devices, Web, organizations. Machine learning algorithms are used to uncover patterns among the attributes of this data. Hence, they can be used to make predictions that can be used by medical practitioners and people at managerial level to make executive decisions. Not all the attributes in the datasets generated are important for training the machine learning algorithms. Some attributes might be irrelevant and some might not affect the outcome of the prediction. Ignoring or removing these irrelevant or less important attributes reduces the burden on machine learning algorithms. In this work two of the prominent dimensionality reduction techniques, Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA) are investigated on four popular Machine Learning (ML) algorithms, Decision Tree Induction, Support Vector Machine (SVM), Naive Bayes Classifier and Random Forest Classifier using publicly available Cardiotocography (CTG) dataset from University of California and Irvine Machine Learning Repository. The experimentation results prove that PCA outperforms LDA in all the measures. Also, the performance of the classifiers, Decision Tree, Random Forest examined is not affected much by using PCA and LDA.To further analyze the performance of PCA and LDA the eperimentation is carried out on Diabetic Retinopathy (DR) and Intrusion Detection System (IDS) datasets. Experimentation results prove that ML algorithms with PCA produce better results when dimensionality of the datasets is high. When dimensionality of datasets is low it is observed that the ML algorithms without dimensionality reduction yields better results. INDEX TERMS Cardiotocography dataset, dimensionality reduction, feature engineering, linear discriminant analysis, machine learning, principal component analysis.

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Section: Literature Reviewmentioning

confidence: 99%

Analysis of Dimensionality Reduction Techniques on Big Data

et al. 2020

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“…In recent years, Park et al [ 6 ] applied reverse propagation learning sequential multi-layer perceptron (SMLP) to obtain the prediction probability of diabetes, finding that the results were superior to regression models. Zhu et al [ 7 ] improved the logistic regression algorithm intelligently, and the accuracy improved by 1.98% in diabetes prediction. In addition, Sudharsan et al [ 8 ] used machine learning models to predict hypoglycemia events occurring in 24 h in diabetics, resulting in a sensitivity of 92% and a specificity of 70%.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Type 2 Diabetes Risk and Its Effect Evaluation Based on the XGBoost Model

Wang

Chen

et al. 2020

Healthcare

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In view of the harm of diabetes to the population, we have introduced an ensemble learning algorithm—EXtreme Gradient Boosting (XGBoost) to predict the risk of type 2 diabetes and compared it with Support Vector Machines (SVM), the Random Forest (RF) and K-Nearest Neighbor (K-NN) algorithm in order to improve the prediction effect of existing models. The combination of convenient sampling and snowball sampling in Xicheng District, Beijing was used to conduct a questionnaire survey on the personal data, eating habits, exercise status and family medical history of 380 middle-aged and elderly people. Then, we trained the models and obtained the disease risk index for each sample with 10-fold cross-validation. Experiments were made to compare the commonly used machine learning algorithms mentioned above and we found that XGBoost had the best prediction effect, with an average accuracy of 0.8909 and the area under the receiver’s working characteristic curve (AUC) was 0.9182. Therefore, due to the superiority of its architecture, XGBoost has more outstanding prediction accuracy and generalization ability than existing algorithms in predicting the risk of type 2 diabetes, which is conducive to the intelligent prevention and control of diabetes in the future.

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“…The K-means algorithm is one of the most well-known group analysis techniques (24,25). It is an unsupervised classi cation algorithm, which attempts to nd from n individuals, K non-overlapping groups.…”

Section: Methodsmentioning

confidence: 99%

“…In Eq. 1, is expressed by: 2 It is not uncommon for PCA to be used to project individuals into a smaller subspace and then apply the K-means algorithm in that subspace (25,26). Therefore, the K-means algorithm follows the following steps:…”

Section: Methodsmentioning

confidence: 99%

Contribution to a Mixed Assessment of Patient Safety Culture

Fourar

Djebabra

Benhassine

et al. 2020

Preprint

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Purpose: The evaluation of patient safety culture is conducted using quantitative methods based on the use of questionnaires and qualitative ones focused on the deployment of cultural maturity models. These methods are known to suffer from certain major limits. This article aims to overcome the difficulties encountered by both methods and to propose a novel approach to the assessment of PSC. Methodology: The approach proposed in this article consists of applying a combined method, based on Principal Component Analysis (PCA) and K-means algorithm, to group together PSC dimensions into macro-dimensions whose exploitation allows to overcome the difficulties encountered with dimensional analysis of PSC and then, serve as a basic support for the development of a patient safety culture maturity model. Findings: The results of the combined method PCA / k-means shows that PSC dimensions can be grouped into three macro-dimensions that were capitalized in a first place using factors related to the development of PSC and in a second place to develop a quantitative maturity matrix that helped in the identification of PSC maturity levels.Originality: The merit of our proposal is to work towards a quali-quantitative evaluation of safety culture recommended by a good number of researchers but, to our knowledge, few or no studies are devoted to this hybrid or systematic evaluation of safety culture. Thus, the results can also be projected to implicate PSC actors and to frame the evaluation pf PSC maturity by international standards.

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Improved logistic regression model for diabetes prediction by integrating PCA and K-means techniques

Cited by 173 publications

References 3 publications

Analysis of Dimensionality Reduction Techniques on Big Data

Analysis of Dimensionality Reduction Techniques on Big Data

Prediction of Type 2 Diabetes Risk and Its Effect Evaluation Based on the XGBoost Model

Contribution to a Mixed Assessment of Patient Safety Culture

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